Cognitive Neurosciences and Strategic Management: Challenges and Opportunities in Tying the

Knot

Authors and affiliations:

Daniella LAUREIRO MARTINEZ* ETH Zurich, Switzerland.

Vinod VENKATRAMAN* Fox School of Business, Temple University, Philadelphia, United

States.

Stefano CAPPA IUSS Center for Neurocognition and Theoretical Syntax, Pavia University, Pavia,

Italy.

Maurizio ZOLLO Bocconi University, Milan, Italy.

Stefano BRUSONI ETH Zurich, Switzerland.

*first and corresponding authors – equal contribution

Working Paper – Do Not Cite Without Author's Permission

Running head:

Cognitive Neurosciences and Strategic Management

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Structured Abstract

Conceptual paper

Purpose

This paper discusses the practical challenges and opportunities involved in merging the two fields

of cognitive neurosciences and strategic management, starting from the premise that the need to

marry them is justified by their complementarities, as opposed to the level of analysis on which

they both focus.

Design/methodology/approach

We discuss the potential benefits and drawbacks of using methods borrowed from cognitive

neurosciences for management research.

Findings

First, we argue that there are clear advantages in deploying techniques that enable researchers to

observe processes and variables that are central to management research, with the caveat that

neuroscientific methods and techniques are not general-purpose technologies. Second, we identify

three core issues that specify the boundaries within which management scholars can usefully

deploy such methods. Third, we propose a possible research agenda with various areas of synergy

between the complementary capabilities of management and neuroscience scholars, aiming to

generate valuable knowledge and insight for both disciplines and also for society as a whole.

Originality/value

By focusing on the practical issues associated with the use of neurosciences to answer Page 2 of 25

managerially relevant questions, we hope to help researchers design appropriate research efforts to

take the opportunities while avoiding the pitfalls inherent in this specific type of cross-disciplinary

endeavor.

Keywords: neuroscience, cognition, decision-making, attention, sustainability, fMRI, methods,

opportunities, challenges

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Cognitive Neurosciences and Strategic

Management: Challenges and Opportunities in

Tying the Knot

Daniella LAUREIRO MARTINEZ*, Vinod VENKATRAMAN*,

Stefano CAPPA, Maurizio ZOLLO, Stefano BRUSONI

*first and corresponding authors – equal contribution

Abstract

This paper discusses the potential benefits and drawbacks of using methods borrowed from

cognitive neurosciences for management research. Its aim is threefold. First, we argue that there

are clear advantages in deploying techniques that enable researchers to observe processes and

variables that are central to management research (e.g. cognition, attention, emotions). However,

we should not allow ourselves to be carried away by “neuro-hype.” Neuroscientific methods and

techniques (e.g. fMRI) are not panaceas; they are useful for solving some specific types of

problems, but not all. Second, we identify three core issues that determine the boundaries within

which management scholars can usefully deploy such methods: task selection and replication,

sampling considerations, and ethical implications. Each of these issues presents threats and

opportunities that researchers need to consider. Finally, we propose a possible research agenda that

identifies various areas of complementarity between strategic management and neuroscience,

aiming to generate knowledge and insight for both academic communities, and also for society as a

whole.

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Introduction

The rise of behavioral strategy as a means to further the quest for appropriate microfoundations to

management research has driven the diffusion of new methods for collecting and analyzing new

types of data. This paper provides a critical discussion and evaluation of the possibility of

adapting neuroscientific techniques to understand managerial decision-making, its antecedents,

and its deep processes. Issues of cognition and learning are central to discussions on strategy

(Gavetti, Levinthal, & Ocasio, 2007; Levinthal, 2011). Therefore, bringing in cognitive

neurosciences seems an obvious extension to what management scholars already do. In this paper,

we contend that the use of neuroscientific techniques can play an important role in informing

management research and work on behavioral strategy.

Scholars have speculated that management research requires the use of neuroscience to

study decision-making at the individual level, since strategy research deals with important, often

irreversible decisions taken by key individuals (Becker, Cropanzano,& Sanfey, 2011; Powell,

2011). However, we contend that the individual level of analysis is not the point. There are several

managerially relevant problems occurring at the individual level that do not require neuroscience

techniques; individuals can be studied perfectly well using the numerous management-science

methods already available. Instead, we argue that strategy needs cognitive neurosciences in order to

benefit from that field’s theories and findings, and to add its techniques to the pool of extant

management-science methods. Theories and findings from cognitive neuroscience could serve as

building blocks for the study of important management issues, since it has been established that

cognition and learning are central to strategy. Using cognitive neurosciences techniques would

allow for greater precision, reliability, and cumulativeness in the analysis of certain types of

problem.

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However, applying these techniques poses several challenges. Researchers should use

caution, and be mindful not only of the basic philosophical differences between management and

neuroscience (Healey & Hodgkinson, 2014), but also of the crucial methodological and design

issues they will face. We aim to clarify the types of problems that would benefit from the

incorporation of neuroscientific methods in management research, and offer suggestions for doing

so successfully.

We begin by briefly introducing one of the most common cognitive neurosciences

techniques: functional magnetic resonance imaging (fMRI). We propose three steps for the use of

neuroscientific methods to address strategic management questions. Unlike other discussions on this

topic, we take a highly pragmatic approach, focusing on three specific design issues to be

considered. In our view, we already know a lot about the general issues related to applying these

techniques (e.g. methodological individualism, the danger of reductionism, reverse causality, etc.).

All these challenges can be addressed, but to do so we must look at the detail, wherein hides the

devil. The heart of this paper is therefore structured around three central methodological and design

issues that must be tackled in order to apply neuroscientific techniques in management research:

task design, sampling processes, and ethical implications and underpinnings. We thus clarify the

features that scholars should consider when deciding whether to use these methods for management

research. We conclude by proposing a research agenda for management scholars and neuroscientists

interested in advancing the discussion on cognition and decision-making.

fMRI as a method for strategic management research

Several methods are available to study and understand neurological and physiological

mechanisms of potential interest to social science in general, and management science in

particular. They include EEG, MEG, PET, GSR, and fMRI, among many others. In this piece, we

focus mainly on fMRI because of its current popularity and strong potential for informing

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mechanistic questions in strategic management.

fMRI is a non-invasive method that enables investigators to localize and track changes in

blood oxygenation during ongoing cognitive tasks (Ogawa et al., 1990). The popular blood

oxygenation level dependent (BOLD) contrast, used to measure brain activity, is based on the fact

that hemoglobin has different magnetic properties depending on its state of oxygenation:

oxyhemoglobin is diamagnetic, while deoxyhemoglobin is paramagnetic, and paramagnetic

substances distort the surrounding magnetic field more. When a particular task engages specific

regions in the brain, the brain vasculature responds by increasing the flow of oxygen-rich blood into

those regions. This in turn, leads to a localized increase in BOLD signal intensity in that brain

region, which is measured using high-field magnetic resonance scanners (Huettel, Song, &

McCarthy, 2004). Thus, the BOLD signal represents an indirect and correlative measure of local

neuronal activity.

The typical fMRI response to a single trial or event, known as the hemodynamic response,

begins after a delay of 1–2 seconds, peaks about 5–6 seconds after the onset of the event, and

returns to the baseline (default activation prior to onset of the event) roughly 14–16 seconds from

event onset. The measurement of hemodynamic response can be confounded by several sources of

noise, including machine noise, random white noise, and artifacts such as breathing and heartbeat,

among others. Therefore, several trials are needed to reliably isolate task-related hemodynamic

response from noise. Crucially, the hemodynamic responses to multiple sequential events sum in a

roughly linear manner (Dale & Buckner, 1997; Huettel & McCarthy, 2000), which allows multiple

events to be presented at greater frequency than the 14–16-second duration of a single

hemodynamic response.

Despite their many advantages in terms of localizing brain activity, fMRI studies still

provide only an indirect and correlative measure of underlying neuronal activity. It is not possible to

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attribute causality: the activated regions may be associated with the task, but are not necessarily

essential for it. Also, multiple sampling iterations (i.e. multiple task trials) are required to obtain

reliable estimates for each phenomenon of interest. This is particularly relevant in management and

strategy studies, where researchers need to obtain choice preferences across multiple problems to

achieve reliable neural estimates (a theme we will return to later).

Therefore, we propose three key steps that management scholars considering using fMRI

techniques to answer questions about strategic management should take:

1. Simplify the real-world strategy problem into a cognitive neuroscience-friendly

format. This includes devising repeatable tasks that can be presented sequentially such that

neural correlates for each cognitive process of interest can be isolated experimentally. For

example, if you study market-entry decisions, you need to make sure that specific decision

situations that target precisely the study question are presented a large number of times to

the study participants repeatedly. It is common to present participant with the decisions over

a hundred trials. If one has two types of decisions (say new and old markets), it will be

common to have well over two hundred decision trials, with similar choice alternatives each

time, so that a decisional pattern can be established and the neural correlates statistically

validated vis-à-vis other possible stimuli or noise signals.

2. Identify the neural correlates of the variables believed to predict preferences. Based on

prior literature in neuroscience, the research team (typically made up of both strategy and

neuroscience scholars) will need to specify not only the theoretical variables in the decision

model, but also the neural location of each factor in the model. For instance, Laureiro et al.

(2010) show the neural correlates of the exploration and exploitation decisions of interest in

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their study, based on advancements in neuroscience that refer to similar factors1. This step

allows robust models of behavior to be built that are biologically plausible, based on the

neural correlates identified. However, while identifying neural correlates may be of interest

to neuroscientists, management researchers are more concerned with building reliable

models of behavior that can predict subsequent out-of-sample behavior (Venkatraman,

Clithero, Fitzsimons, & Huettel, 2012) – and this has some ethical implications, as discussed

below.

3. Give participants real incentives to engage fully in the task. Given the repetitive nature

of the tasks typically used in cognitive neuroscience lab studies, it is important to incentivize

participants in order to maintain their engagement and obtain their true underlying

preferences. This is standard practice in well-designed experiments, but becomes

particularly important when the objective is to detect neural activations of interest,

disentangling them from other possible signals produced by the brain. For similar reasons, it

is also particularly important to select a homogenous sample to obtain robust and

meaningful inferences.

Three central issues: task selection and replication, sampling considerations, and ethical

implications

The three steps above are centered on three crucial practical issues – task selection, sampling, and

ethical issues – that must be clearly understood for a successful marriage between cognitive

neurosciences and strategic management. In this section, we discuss these issues and briefly

address the key threats and opportunities inherent in each one.

1 This step might lead to a conceptual/methodological contribution for the strategy field, as an intermediate step towards the publication of the results of the empirical inquiry.

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Task selection and replication

When we think of “strategy” problems, we think of big decisions relating to major initiatives such

as alliances, reorganizations, R&D investments, product launches, etc. Such situations involve

complex and infrequent decisions that have irreversible results (Loasby, 1976). Some believe that

neuroscience can take us “inside the heads” of the business leaders who make these decisions, but

this belief is unfounded. The brain is a notoriously complex organ that performs several different

functions simultaneously. For example, how do we isolate activations related to negotiating a

strategic alliance from an intervening background thought about what time to have lunch? The

usual approach adopted in neuroscience to tackle this fundamental issue is to design experiments

that require multiple iterations of the core task (e.g. alliance design) to reliably isolate activations

related to the process of interest. Crucially, these iterations need to be designed such that the

participants remain engaged with the process, even as they make very similar decisions over and

over again. Given these constraints, we should also consider whether deploying such expensive

techniques really makes sense for management scholars. We contend that progress needs to be in

small steps rather than giant leaps.

First, the management tasks used for neuroscience studies must be simple and repeatable.

The design challenge is to identify the core elements of a complex business decision so the

participant can carry out a simple task while in the fMRI scanner. For example, if we want to study

how individual managers make decisions related to M&A processes, we need to specify precisely

which decision we want to explain, and what are its cognitive or emotional antecedents. Let’s say

we want to study how managers decide whether the top management team of the acquired company

should be retained or replaced. We need to create a (simulated) scenario in which this decision is

made several times, perhaps in different situations. The differences between situations should be

theoretically meaningful for the experimental manipulation – so, for instance, they might relate to

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the performance of the acquired company, its size, its product relatedness, or its geographic

location. But the focal decision must always be the same, sufficiently simple and clear, and related

to specific cognitive and rational drivers that can be isolated from the myriad neural systems that

could potentially be activated when a decision to replace or retain the acquired top management

team is made.

In another example, if the problem is framed in terms of trust, we might implement a multi-

round version of a modified dictator game called the “trust game” (King-Casas et al., 2005). In each

round, subjects make decisions about whether or not to trust their investments to different

individuals, based on their profiles. The use of different profiles across different rounds allows the

decision to be repeated while keeping the participant engaged and interested. However, use of the

trust game precludes any discussion about risk, which would require a different task (Huettel,

Stowe, Gordon, Warner, & Platt, 2006; Venkatraman, Payne, Bettman, Luce, & Huettel, 2009). It is

also possible to use a combination of tasks to isolate the specific underlying processes and

mechanisms (Venkatraman, Rosati, Taren, & Huettel, 2009).

A good starting point for management researchers who want to identify appropriate tasks is

to try to understand how neuroscientists approach these problems. Initially, neuroscientists rely on,

and control for, a relatively large range of empirical regularities that generally hold true. For

example, it is commonly accepted that there is a strong positive correlation between measures of

intelligence and performance in the working memory (N-back) task, where participants are

presented with a series of letters from the alphabet and asked to indicate whether the current

stimulus matches the one presented “N” trials back. Hence, it is possible to design tasks that ignore

IQ results if they include controls such as N-back. There are few tasks in the context of management

research that are of comparable robustness and regularity, particularly at the individual level.

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Sampling considerations

It is important to observe that (theoretical) sampling decisions are related to the choice of

processes to observe in an organization, and understanding how to select the relevant processes to

observe in a neuroscientific study is crucial.

A large multinational corporation is likely to include managers who are diverse in terms of

their culture, educational background, age, and gender. If we are interested in studying the neural

correlates of their decision-making outcomes, we need to pay particular attention to the size and

homogeneity of the sample.

In terms of sample size, most fMRI studies involve relatively small samples of 15 to 20

participants (Yarkoni & Braver, 2010). The two main reasons for this are the complexity of

conducting the studies and the costs per participant. The primary aim of these studies is to

understand consistent activations across the sample – that is, the epistemological focus is on the

location and magnitude of the mean effect (neural activation) of a given decision or action, rather

than explaining the variance in behavioral or performance outcomes across the sample. For

example, in a neuroscientific study of risk, the primary objective is to identify the regions of the

brain that are consistently associated with risky decisions.

Some recent work has used slightly bigger samples (30 individuals) to try and understand

individual variability (Laureiro-Martínez et al., 2014; Venkatraman, Huettel, Chuah, Payne, &

Chee, 2011; Venkatraman, Payne, et al., 2009). In this research, the emphasis is on understanding

how individual differences in risk propensity lead to modulation of activity in the brain regions

associated with risk processing. If we can isolate these regions reliably, it is conceivable that these

measures could be used as reliable indices to predict performance in different management

scenarios. This raises several ethical concerns, which are discussed in the next section.

A third class of studies seeks to understand differences between two groups of individuals. Page 12 of 25

This is similar to patient and clinical studies in neuroscience, which involve people with various

disorders (Korn, Sharot, Walter, Heekeren, & Dolan, 2014; Schlagenhauf et al., 2014). In these

studies, a separate homogenous group of individuals with a specific disorder is often compared to a

closely matched cohort of healthy individuals to identify differences in activations and develop

possible interventions. Individuals are matched on a series of variables that the researchers want to

control for; age and gender are used in many cases.

Similar paradigms can be extended to management problems where two groups of “healthy”

individuals are compared against each other, using specific performance metrics to rank them.

However, these metrics are often subjective and complex to develop. Therefore, the sampling

techniques used in typical neuroscience experiments are closer to theoretical sampling than

statistical sampling, and the emphasis is often on experimental control and comparability rather than

representativeness.

Identifying the specific categories with which to replicate a certain phenomenon/outcome in

healthy individuals is very similar to the logic deployed in case-study research where participants

are identified to exclude trivial explanations and guarantee comparability of results (Eisenhardt &

Graebner, 2007; Yin, 2010). At this stage of technological development of fMRI research, the

samples will necessarily be small. In our view, it is important that the management community

tackles this issue, in order to understand what can be gained from using these methods. There is a

need for precision and observability (hence the relevance of the comparison with qualitative

methods), with less interest in generalizability and external validity.

This discussion of sample sizes and sampling techniques highlights a dilemma related to the

homogeneity of the sample. Based on the last 10 years of neuroscience and psychological research,

we know that factors such as age, gender, education, and language have significant impacts on

psychological processes and neural activations. For example, there is considerable evidence that

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aging alters the patterns of brain activity associated with many cognitive tasks, even if we only

consider healthy individuals (Cabeza, 2002). Most of the evidence derives from memory studies,

which generally involve greater activation of a broader set of brain regions associated with

comparable levels of performance (for a comprehensive review, see Rossini, Rossi, Babiloni, &

Polich, 2007). This is often taken to indicate “compensatory” engagement of more brain tissue to

counteract the effect of neuronal cell loss associated with aging. Similarly, the impact of education

and occupation derives mostly from clinical or neuropsychological studies that assess the role of

cognitive reserves as a factor of resilience to counteract advancing neurodegenerative pathology

(see, e.g. Garibotto et al., 2008). Many studies report gender differences in brain activity in relation

to women showing more bilateral activity than men in language-processing tasks (however, see the

negative results of the meta-analysis reported by Sommer, Aleman, Bouma, & Kahn, 2004). More

reliable differences have been reported in the domain of emotional processing (see, e.g., Schulte-

Ruther, Markowitsch, Shah, Fink, & Piefke, 2008), especially in relation to subjects’ personality

traits and genetic background (Brown & Hariri, 2006). Finally, linguistic background (monolingual

vs. bi- or multilingual) is an important modulator of brain activity in tasks that engage language

processing (see review in Abutalebi, Cappa, & Perani, 2001).

Given the range of variability across demographics, the classic neuroscientific response is

often to exclude people with certain characteristics from the sample (i.e., to choose a more

homogenous sample). The key objective of many neuroscience experiments is understanding how

the brain works under normal circumstances, with normality grounded mainly in how often

individuals with certain traits appear in the population. These criteria might appear odd to

management scholars: variables such as age, which is often correlated with experience and tenure,

are clearly important to them. On the other hand, it is far less intuitive for management scholars to

make sense of individual differences such as bilingualism. For instance, one would expect

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bilinguals to be highly represented in large multinationals – should we exclude them from our

studies since we expect differences in brain activation?

We conclude our discussion on sampling by highlighting the crucial distinction between

structural and functional differences in brain activations. In the case of aging, many of the

differences observed may be due to structural changes such as brain atrophy associated with age.

Alternatively, the differences in brain activations may represent systematic differences in the way

people approach a particular problem – i.e. functional differences. While structural changes may be

of greater concern from a sampling perspective, especially when using smaller samples, we contend

that functional differences can and should be exploited more in management research. For example,

if we know that two different firms approach the same problem from different perspectives, one of

which leads to better results and more effective decisions, this is something to learn from. However,

a more heterogeneous sample requires a larger sample size, and hence higher costs. The dilemma

between using homogeneous (excluding participants who may be outliers to the problems of

interest) versus heterogeneous samples raises other ethical concerns.

Ethical implications

In this section we identify four major ethical issues associated with the application of cognitive

neuroscience methods to management research.

Abnormalities

The first issue relates to the appearance of physical abnormalities in brain scans. Approximately

1% of individuals have brain abnormalities, which raises at least two ethical implications. Firstly,

since most of these abnormalities are discovered after data collection, we need to address the issue

of “incidental” findings. In most cases, study participants are unaware of any abnormalities, and

the researchers are rarely qualified to make diagnostic inferences. So do they have a moral

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obligation to advise the participant to seek further advice? The answer depends on whether, prior

to the scan, the researcher obtained the participant’s agreement to be contacted in the case of

unexpected results. Another key question is whether the researchers involved are trained in coping

with emotional situations in a rational and effective manner. If not, should all scans include

clinical oversight by trained physicians?

The second issue is related to whether data on individuals with abnormalities should be

included in the subsequent analyses. As discussed in the section on sampling, what is critical is

whether the research question focuses on understanding individual differences. If the interest is in

aggregate performance, it might be better to preserve a tightly homogenous sample and exclude

individuals with abnormalities. Here, we should emphasize the similarities with qualitative research,

which traditionally focuses on studying unique events and organizations.

Resources

The second ethical issue relates to the resources required to perform an fMRI study, and the

opportunity costs of accessing them. This issue has three dimensions: the opportunity cost of the

fMRI laboratory, the total opportunity cost of the study, and the opportunity cost of participants.

1. Laboratory-related opportunity costs. Studying 18 managers using fMRI occupies

the scanner facilities, as well as its attendant technicians, for at least 18 to 25 hours,

during which they might otherwise be used to care for patients. To mitigate this

issue, it may be possible for a study of managers to involve researchers interested in

various other questions – and individuals – that could be synergistically transformed.

One solution would be to coordinate scanner access to coincide with times that are

unlikely to be useful to patients – for example, outside clinicians’ working hours, or

during other periods when the facilities are not available for patient use. It might be

that these times would also suit the populations of interest to strategic management Page 16 of 25

researchers, and avoid clashes with normal working hours.

2. Study costs. One might wonder about the costs of an fMRI study. While prices vary

across different laboratories from less than hundred to several hundreds of dollars,

the total cost will depend mainly upon the agreements made between the researchers

and the specific lab. As with other techniques, researchers must consider not just the

total cost of performing the study, but also whether the technique is the most

appropriate to address the research question. To continue the comparison with

qualitative techniques, the cost of using a scanner for an fMRI study is comparable

to the costs of in-depth parallel case studies that include travel costs, interview costs,

transcription costs, software for analysis, and so on.

3. Participants’ opportunity cost. Unlike patients taking part in medical research,

management study participants are often volunteers who are willing to give up their

time out of the motivation to collaborate with the researchers, at the expense of

devoting that time to their work or personal lives. In particular, the subjects of

strategic management studies may hold very senior managerial positions and have

very busy work schedules, making the opportunity cost of their time very high.

Researchers should discuss what can be done to encourage participants to join the

study, and, most importantly, keep them motivated during the tasks, especially if the

design includes randomized controlled trials with interventions occurring between a

baseline and a post-intervention data collection round. The possibilities will vary,

and depend greatly on the researcher’s understanding of the sample population.

Offering payment to offset participants’ opportunity costs might not be feasible, and

researchers will probably have to rely on non-monetary incentives such as sharing

the study results with participants, or giving them personalized feedback on how

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their results compare to the rest of the sample and/or the broader population.

Generalizability

The third ethical implication derives from the fact that the brain responses that constitute the

results of fMRI studies are obtained from a small group of subjects and then generalized to larger

populations. Management is inherently normative, while cognitive neuroscience is descriptive,

which poses a challenge. Our findings might be used to advise organizations about how to

increase their profits, and this may be at the expense of the individual participating in the study, or

others like them. As management scholars, what should we do when we identify a key neural

correlate and this finding is used to select, hire, or promote certain people at the expense of

others? Does this constitute selection, or discrimination? In our view, it is selection if the

individual can train and deliberately improve performance in the brain systems identified as

relevant for predicting performance, but discrimination if they cannot. We know that some brain

systems are plastic, even in adults, while others are less so. So the system of interest is an ethical

design choice that is made before embarking on the study; relegating ethical issues to the end of

the study is unethical since the choices are irreversible.

Neuro-hype

Finally, it is important to bear in mind that coverage of neuroscience studies in the popular media

tends to make a phenomenon seem real, objective, or effective to the public merely because it

involve activation of the brain – a concept termed “neuro-realism” (Racine, Bar- Ilan, & Illes,

2005). Neuro-realism refers to the uncritical way in which the findings of neuroscientific

investigations are seized upon as validating (or invalidating) our worldview. It is based on the

naïve argument that “if the brain says so, it must be true,” and is grounded in the belief that the

colorful blobs on an fMRI scan represent “visual proof” of the underlying brain processes,

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regardless of the huge complexity of data acquisition and analysis. Thus, such media reporting

raises ethical issues about the way neuroscience findings are disseminated, especially to the

general public. Additional considerations include the basic implications of gaining knowledge

based on a better understanding of how our brains work. For example, the Human Brain Project

(HBP) devotes 3% of its budget to its Ethics and Society Program, whose goals include debating

social and ethical issues among HBP researchers and stakeholders more widely.

We should also take account of learning from other fields, such as genetics. By

contemporaneously considering the various issues in proactive, multidisciplinary dialogue, we

should be able to develop more acceptable and relevant standards than if we allow them to emerge

later through external processes. Finally, disciplines and roles other than researchers – policy

makers, lawyers, journalists, and most importantly, organizational leaders and managers – need to

be involved in the discussion to represent the broader public. There is a common misconception that

ethics discussions should be reserved for philosophers; we believe everyone should play an active

role.

Cognitive neuroscience and strategic management: the road ahead

So far, our discussion has dwelt on things that we, as management scholars, should not do, or

don’t know how to do. We would now like to turn that on its head, and argue that by recognizing

our current limitations, and the persistent tensions in our situation, we can actually move forward

quickly and safely. The points here are not exhaustive; rather, they are intended to hint at the array

of possibilities that lie ahead of us.

First, neuroscience methods can enable and oblige us to clarify what we mean by strategy. If

strategy is seen as something that is continuously formulated, bottom-up, and adapted in response to

environmental circumstances – that is, very much in line with behavioral approaches (as in e.g.

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Cohen, 2007; Cyert & March, 1963; Foss, Heimeriks, Winter, & Zollo, 2012; Gavetti & Rivkin,

2007; Levinthal, 2011; Winter, 2013) – then we are all on common ground. However, if strategy is

understood as one-off, top-down decision-making (Ansoff, 1980, 1987) then there is a conflict. The

value of neuroscience for management research lies in the opportunity to closely examine the

decisions made by managers in relevant, albeit simulated, contexts, employing a behavioral lens. In

other words, neuroscience and its methodologies should not be employed because managers are

individuals, and hence can be “studied.” They should be used because we believe that over the long

term, big strategic decisions comprise a series of small, repeatable decisions, and that issues and

processes related to experience and learning matter. If we share this belief, the next step is to design

a task that captures these small decisions in an incentive-compatible manner, to be studied using

brain imaging.

Second, an alternative way to use neuroscience in strategic management research is to

measure individual-level antecedents to strategic decision-making, and, indirectly, performance

outcomes. In other words, brain imaging can provide objective and extremely precise measurements

of how individual managers’ brains activate in the presence of a given stimulus – not just while they

are making a specific decision, or carrying out a strategically relevant action. For example, if one

studies cooperative strategies, it might be particularly relevant to gauge managers’ strength of

empathic reactions to a standard set of stimuli (e.g. vignettes, pictures, short movies, etc.) to explain

variation in, say, design decisions (e.g. equity or governance arrangements) or even in alliance

performance. Compared to the alternative of relying on self-reported assessments, or even

psychological scales, the objectivity and precision of brain imaging techniques can be a particularly

strong advantage in a study design. Incidentally, one advantage of using brain imaging techniques

together with standard scales to measure a given construct lies in testing the capacity of

psychological scales to capture the variance in the same theoretical constructs. Once the scales had

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been validated via correlational evidence with neural correlates, it would be possible to simplify

future research designs that make use of them, especially for control measures.

A third area of contribution relates to the study of learning and capability building

processes. In this case, the advantage offered by neuroscientific methodologies, and related bodies

of knowledge, lies in the objective and quantitative assessment of learning. Here, the phenomenon

of learning is defined as the variation of neural activation, or even of neural density, in the areas of

the brain where the relevant cognitive or emotional processes reside, following a specific type of

training or intervention aimed at developing knowledge. Whereas, in the first two approaches

described above, the advantage of neuroscience lies in its superior “mapping” of individuals’

strategic decisions, or of the neuropsychological antecedents of those decisions (emotions,

cognitions, etc.), here our quest is to evaluate changes in neural responses in the “mapped” areas

consequent to learning experiences. By carefully designing their experimental settings (sampling,

randomization of allocation of interventions, active and passive control groups, etc.), management

researchers can discover how effective alternative learning approaches are in developing specific

strategic capabilities.

For example, suppose we were interested in studying competitive strategy interactions in a

specific game-theoretic context. We could assess whether the capability to, say, anticipate a longer

series of future interactions, as chess players do, is located in regions of the brain that neuroscience

literature says should activate when managers exercise these “executive functions” (as

neuroscientists call this type of highly evolved cognitive skill). More importantly, management

scholars will be able to tackle questions such as: How do managers develop these highly relevant

cognitive skills? What are the most effective training approaches for developing them? And, finally,

what are the performance implications of these learning processes?

To give another, real-world example, in our study of exploration and exploitation decisions

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(Laureiro et al. 2014), the first phase was to “map” these decisions to the underlying

neurophysiological correlates in the brains of decision-makers (first students, and then actual

managers and entrepreneurs). The selection of the four-armed bandit game, with its simple,

repeatable task structure that generates feedback and enables individuals to learn over time, lent

itself to studying the performance implications of the capability of switching between the two

modes of attention modulation (J. D. Cohen, Aston-Jones, & Gilzenrat, 2004; Daw, O’Doherty,

Dayan, Seymour, & Dolan, 2006; Laureiro-Martínez, Brusoni, & Zollo, 2010; Laureiro-Martínez, et

al., 2013; Laureiro-Martínez et al., 2014; McClure, Gilzenrat, & Cohen, 2006). However, these

same characteristics also allowed us to explore the effectiveness of two very different approaches to

the learning problem, one based on neuro-cognitive training (so-called “brain training”) and the

other based on meditative and introspective training, in a classical randomized controlled trial

design.

In a currently ongoing project, aimed at studying stakeholder strategy in an environmental

sustainability context, we approached the research design with a similar two-step approach. The

first phase was to identify of the neural correlates for this type of strategic decision, using the “fish-

bank” simulation, which poses “tragedy of the commons” problems in an iterative game context. As

well as validating the neural “mapping” of these business decisions, we wanted to verify the link

between the activation of specific brain regions and the quality of the decisional outcomes, i.e. the

sustainability performance generated. The second phase was a randomized controlled trial with the

same type of learning interventions, involving both neurocognitive and meditative training, to assess

their comparative effectiveness and in a passive control group.

So much for the potential of applying neuroscience to strategic management studies. But what

about the converse? What might managerial scholarship contribute to the advancement of

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neurosciences? The contributions made by economics and decision-making to cognitive

neuroscience are manifest (Clithero, Tankersley, & Huettel, 2008; Huettel, 2010), and there is no

obvious reason why equally valuable contributions could not come from our field. For instance,

consider the problem of aggregating individual responses up to group or even organizational

level. Anthropomorphic metaphors, which attribute traits identified at the individual level to entire

organizations, are not particularly helpful. For example, consider the terminology of “hot” and

“cold” rationality, or attempts to locate the organization’s metaphorical “left brain.”

Neuroscientists are often interested in explaining macro-level phenomena (e.g. why people eat

junk food when they know it is bad for their health (Hare, Camerer, & Rangel, 2009)) by

observing a few individuals and scaling up to a whole population. Such an aggregation process

makes sense for relatively well-structured problems, but works less well in the case of complex

organizations, where interdependencies and emergent properties play a crucial role in explaining

behavior. Our somewhat contrarian suggestion is that we should not look for similarities across

levels of analysis, but rather for differences – because it is these that hold the key to the

aggregation problem. Below we provide two examples to illustrate our thinking.

First, let us consider attention, a central construct in strategic management research and one

of the most studied topics in cognitive neurosciences (Ocasio, 2011). Studies of sustained attention

and cognitive control have found that when an individual focuses their attentional resources on a

problem, speed and accuracy will be high – and this holds for a range of tasks and for many

different samples (Norman & Shallice, 2000; Posner & Petersen, 1990), This is in line with a recent

study of a sample of experienced organizational leaders: those with higher attention control were

both faster and more accurate (Laureiro-Martínez, et al., 2013). Now, consider an organization that

devotes a great deal of attentional resource to solving a particular problem – for example, by

organizing a series of meetings involving the top management team, and carefully evaluating the

Page 23 of 25

various options before coming to a decision. The sequential and coordinated attention applied by

General Electric (GE) leaders to the issues faced by the firm (Joseph & Ocasio, 2012) is a case in

point. This appears to be the opposite of a quick decision process, but why? What changes when we

move from the individual to the organization? Clearly, there are issues related to how people

interact, and act together, that cannot be explained through a simple head count of the number of

individuals involved. For this class of problems, management scholars can provide insights to their

neuroscience colleagues. For example, based on Joseph and Ocasio’s (2012) findings for GE

leaders, one could design an fMRI task that requires individuals to focus their attention sequentially

and in a coordinated manner, with the aim of predicting performance in the deliberative and

iterative types of decision process observed at GE.

Second, recent work by Francesca Gino and colleagues (Gino & Ariely, 2012) argues that

creative individuals seem more likely to exhibit unethical behavior. Creative people think “broadly”

and “outside the box,” even when considering unethical options. This is an interesting result at the

individual level – so what are the implications for organizations that engage in continuous efforts to

foster their members’ creativity? While individual-level cheating might be tolerated, organization-

level cheating is not an option. What structures and processes can be deployed to reconcile

seemingly conflicting individual- and organizational-level aims?

In conclusion, we hope this paper advances the discussion on how strategic management research

can potentially benefit from the techniques and findings of cognitive neuroscience, and the

practical opportunities and challenges that researchers will face when they collaborate across the

two fields. In our view, future “boundary-spanners” would do well not to rely solely on the

argument that strategy needs neuroscience because it deals with important decisions taken by key

individuals. Furthermore, we hope these researchers will have a genuine interest in the theories,

Page 24 of 25

findings, and methods of neuroscience, and recognize their importance in developing a

comprehensive pool of research methods for studying individuals. Each method will have its own

strengths and weaknesses, its strong complementarities and its useful redundancies. We are

convinced that by being clear on these complementarities, and respecting the methodological and

ethical boundaries discussed above, we can make real scientific progress that will benefit not only

our respective domains, but society as a whole.

Page 25 of 25

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